How I Saved $500/Month on AI Costs (Case Study)

The Problem

Six months ago, I was running a customer support chatbot for a SaaS product. Nothing fancy - just an AI assistant that helped users troubleshoot common issues, answer FAQs, and escalate complex problems to human agents.

The bot was working great. Users loved it. Support tickets dropped by 40%. But there was one problem: the AI bill was eating our margins alive.

Step 1: Analyzing the Usage

First, I exported a week of request logs and categorized them. Here's what I found:

Request Type	% of Requests	Complexity
Simple FAQs	45%	Low - could be cached
Account lookups	25%	Low - just data retrieval
Troubleshooting	20%	Medium - needs reasoning
Complex issues	10%	High - needs GPT-4

The insight: 90% of requests didn't actually need GPT-4. I was using a $0.03/1K token model for tasks that could be handled by a $0.00015/1K model. That's 200x more expensive than necessary.

Step 2: Implementing Smart Routing

Instead of rebuilding my entire system, I switched to TokenSaver's API. The change was simple - just swap the endpoint:

That's it. One endpoint change. TokenSaver automatically routes each request to the cheapest provider that can handle it.

Step 3: The Results

Here's what happened in the first month:

Where Did the Savings Come From?

Looking at my TokenSaver dashboard, here's how requests were distributed:

Provider	% of Requests	Why
Google Gemini 2.0 Flash	62%	Free tier handled simple queries
OpenAI GPT-4o Mini	28%	Medium complexity at low cost
Claude 3.5 Sonnet	7%	Technical troubleshooting
OpenAI GPT-4o	3%	Complex edge cases only

The key insight: 62% of my requests were handled for free by Gemini's experimental tier. The previous month, those same requests cost me $400+ on GPT-4.

Did Quality Suffer?

This was my biggest concern. Here's what I measured:

• User satisfaction score: 4.3/5 (unchanged from before)
• Escalation rate: 12% (slightly better, down from 14%)
• First-response resolution: 73% (unchanged)
• Response time: 1.2s average (slightly faster)

Users couldn't tell the difference. In fact, response times improved slightly because Gemini's infrastructure is incredibly fast.

Unexpected Bonus: Reliability

A week after switching, OpenAI had a 2-hour outage. In the past, this would have meant 2 hours of angry users and missed support tickets.

With TokenSaver? Zero downtime. The system automatically routed around OpenAI and used Anthropic and Google instead. I only found out about the outage from Twitter - my dashboard showed uninterrupted service.

What I'd Do Differently

Looking back, I waited too long to optimize. I spent months "planning to look into it" while burning $500+/month unnecessarily. Here's my advice:

1. Don't over-engineer. I thought I'd need to build complex routing logic. Turns out, switching to a routing service took 10 minutes.
2. Audit your usage first. Understanding that 90% of my requests were simple changed everything.
3. Test with real traffic. Start with 10% of requests on the new system, verify quality, then ramp up.
4. Monitor costs weekly. I now check my dashboard every Monday. Catches issues before they become expensive.

The Numbers, One Year Later

Since making this switch 12 months ago:

That $6,000 went into product development instead of API bills. And the reliability improvements meant fewer late-night pages when providers went down.

Try It Yourself

If you're spending more than $100/month on AI APIs, you're probably overpaying. TokenSaver offers 30 free requests to test with your actual workload - no credit card required.

The switch took me 10 minutes. The savings started immediately.